I do usually have a beer at the airport, but having more means you have more usage out of the plane bathroom which I can do without.

It does occur to me that those following the advice should be seated on the aisle, yes.

I like the saying - weeks of coding can save us hours of planning.

Why do you expect an LLM to provide an accurate distance metrics?

I have recently joined a corporation after years in start ups and 99 % of these apply too.

France is all about big government

Because it doesn't seem to work?

I don't think it's about perfect predictions. It's more about going all in on Metaverse and then on AI and backtracking on both. As a manager you need to use your resources wisely, even if they're as big as what Meta has at its disposal.

The other thing - Peter's principle is that people rise until they hit a level where they can't perform anymore. Zuck is up there as high as you can go, maybe no one is really ready to operate at that level? It seems both him and Elon made a lot of bad decisions lately. It doesn't erase their previous good decisions, but possibly some self-reflection is warranted?

Having worked in such companies, switching to that mode requires very different processes.

> MOOCs never achieved the transformative potential promised during the early hype.

I would disagree, I saw a lot of people, especially in the Data Science field that got up-skilled by back then free Coursera.

The early hype of MOOCs wasn't "a few people in handful of fields will get up-skilled".

If that was all they claimed there wouldn't have been early hype around transformative potential.

Coursera had 1.7 million "students" in under 2 years and was growing faster than Facebook. The President of edX talked about "disrupting" the entire higher education system. Stanford, MIT, Princeton, Brown, Columbia, and Duke all offered MOOCs with the idea that anyone anywhere in the world could now get an MIT/Stanford level of education without needing to be on-campus (or pay a lot of money in tuition).

From the 2012 NYTimes article on MOOCs:

'Dr. Agarwal predicts that “a year from now, campuses will give credit for people with edX certificates.” He expects students will one day arrive on campus with MOOC credits the way they do now with Advanced Placement.'

Never happened.

'Dr. Stavens promises more change, and more disruption: “We are only 5 to 10 percent of the way there.”'

Turned out we were already 100% of the way there after 24 months of MOOCs being launched.

Agarwal said that in public. Behind closed doors, he was completely cynical about impact. His primary goal was financial (for himself).

What happened was primarily due to personalities and misaligned incentives.

Well if that was the case, I'd call him full of shit masquerading as educator.

Exactly.

The hype was massive. Everyone was supposed to be going MOOCs way. It was supposed to restructuring education system grounds up.

Now all I have seen is many did these big data/ data science courses and joined that great enterprise IT boondoggle of data processing/analytics.

I both agree and disagree.

In the early days, Coursera was transformative. I took some amazing courses and learned a lot.

...but the vision was always automation and as courses were rerun, the instructors were more likely to be disengaged, and live interaction via the forums fell. Finally when courses went to ongoing enrollment, there were no longer cohorts, and the experience was a correspondence course.

So in the end it was not transformative. It is pretty much just Youtube plus a website plus a payment platform. A less expensive, less interactive, unaccredited version of Phoenix University.

There's nothing wrong with teachers getting paid. It is the standard model. But of course the standard model is the standard model, not transformative.

Interesting point

Alternate theory, MOOCs got lectures put onto YouTube.

And this is what the early success really was.

It'd be interesting to see if what they "up-skilled" on is now common knowledge in Data Science.

I think especially for Data Science, a lot of the curriculum got taken over by universities and new university programmes for Data Science were created since then so in a way it became common knowledge for fresh graduates.

> Nobody wants people who can do easy things, people want people who can do hard things.

No, people want people that can provide value regardless of the difficulty. What you're describing is how we end up with not invented here syndrome.